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dc.contributor.authorRobinson, Peter M.en_US
dc.date.accessioned2011-03-25en_US
dc.date.accessioned2012-10-16T13:11:09Z-
dc.date.available2012-10-16T13:11:09Z-
dc.date.issued2011en_US
dc.identifier.pidoi:10.1920/wp.cem.2011.1111en_US
dc.identifier.urihttp://hdl.handle.net/10419/64675-
dc.description.abstractNonparametric regression with spatial, or spatio-temporal, data is considered. The conditional mean of a dependent variable, given explanatory ones, is a nonparametric function, while the conditional covariance reflects spatial correlation. Conditional heteroscedasticity is also allowed, as well as non-identically distributed observations. Instead of mixing conditions, a (possibly non-stationary) linear process is assumed for disturbances, allowing for long range, as well as short-range, dependence, while decay in dependence in explanatory variables is described using a measure based on the departure of the joint density from the product of marginal densities. A basic triangular array setting is employed, with the aim of covering various patterns of spatial observation. Sufficient conditions are established for consistency and asymptotic normality of kernel regression estimates. When the cross-sectional dependence is sufficiently mild, the asymptotic variance in the central limit theorem is the same as when observations are independent; otherwise, the rate of convergence is slower. We discuss application of our conditions to spatial autoregressive models, and models defined on a regular lattice.en_US
dc.language.isoengen_US
dc.publisherCentre for Microdata Methods and Practice (cemmap) Londonen_US
dc.relation.ispartofseriescemmap working paper CWP11/11en_US
dc.subject.jelC13en_US
dc.subject.jelC14en_US
dc.subject.jelC21en_US
dc.subject.ddc330en_US
dc.subject.keywordNonparametric regressionen_US
dc.subject.keywordSpatial dataen_US
dc.subject.keywordWeak dependenceen_US
dc.subject.keywordLong range dependenceen_US
dc.subject.keywordHeterogeneityen_US
dc.subject.keywordConsistencyen_US
dc.subject.keywordCentral limit theoremen_US
dc.titleAsymptotic theory for nonparametric regression with spatial dataen_US
dc.typeWorking Paperen_US
dc.identifier.ppn654842795en_US
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungenen_US
Appears in Collections:cemmap working papers, Centre for Microdata Methods and Practice, Institute for Fiscal Studies (IFS)

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